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Deformable Medical Image Registration: A Survey
Open accessAbstract
Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.
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“deformable image registration (Sotiras et al., 2013) is a core component of all MAS methods, and the quality of individual registrations has a very high impact on the accuracy of the final segmentation.”
From MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection (2016) · cites this paper as Sotiras et al., 2013
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Optimization by Simulated Annealing
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983) · Science · doi:10.1126/science.220.4598.671 · Cited by 45,134 · cited 1×
“metallurgy, Simulated Annealing [386] should be referenced.”
New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)
Eisenhauer, E. A., Therasse, P., Bogaerts, J., Schwartz, L. H., Sargent, D., Ford, R., Dancey, J., Arbuck, S., Gwyther, S., Mooney, M., et al. (2009) · European Journal of Cancer · doi:10.1016/j.ejca.2008.10.026 · Cited by 29,534 · cited 1×
“tumor progression [5]), it is necessary to align longitudinal data into a common domain where comparisons are meaningful.”
BackgroundA Method for Registration of 3-D Shapes
Besl, P., & Mckay, N. D. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.121791 · Cited by 22,163 · cited 1×
“One of the most well known approaches is the Iterative Closest Point (ICP) method proposed by Besl and McKay [310].”
MethodNumerical recipes in C: the art of scientific computing
(1993) · Choice Reviews Online · doi:10.5860/choice.30-5638 · Cited by 17,933
(1988) · Cited by 16,582
Speeded-Up Robust Features (SURF)
Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.09.014 · Cited by 13,557 · cited 2×
“Han extended the SURF descriptor [258] to and used it in a hybrid registration framework [267].”
MethodA Combined Corner and Edge Detector
Harris, C., & Stephens, M. (1988) · Procedings of the Alvey Vision Conference 1988 · doi:10.5244/c.2.23 · Cited by 12,901 · cited 1×
“proposed to identify corners by exploiting the information conveyed by the structure tensor [245].”
BackgroundA Stochastic Approximation Method
Robbins, H., & Monro, S. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729586 · Cited by 9,643 · cited 1×
“The last method was proposed by Robbins and Monro (RM) [380].”
MethodMethods of conjugate gradients for solving linear systems
Hestenes, M. R., & Stiefel, E. L. (1952) · Journal of research of the National Bureau of Standards · doi:10.6028/jres.049.044 · Cited by 8,142 · cited 1×
“Among the well-known formulas for β t , one may cite the Fletcher-Reeves [404], the Polak-Ribi`ere [405], the Polak-Ribi`ere-Polyak [406] and the Hestenes-Stiefel [407].”
Method
Flagged first, then by citations
13 of the 539 references couldn’t be matched to a known paper.
Research works printed but not linked: 7
- han, 2010, Feature-constrained nonlinear registration of lung CR images, Med Image Anal Clinic A Grand Challenge, 63
- pollak, 1969, Note sur la convergence de méthodes de directions conjuguées, Revue Franaise informatique de recherche operationnelle, 3, 35
- juan, 2009, A comparison of sift, PCA-sift and surf, Int J Image Process, 3, 143
- 10.1201/9781420042474 · DOI
- vandemeulebroucke, 2007, The popi-model, a point-validated pixel-based breathing thorax model, Int Conf Use Comput Radiat Therapy, 195
- 0, Internet brain segmentation repository
- kwon, 2011, Nonrigid image registration using higher-order MRF model with dense local descriptor, Proc Comput Vis Pattern Recognit Workshop
Cited by
Sorted by citations
Items 201–300 of 1,001
Joshi, A., & Hong, Y. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102917 · Cited by 36
“cn (Yi Hong) ical image analysis and has been an active research field for years (Sotiras et al., 2013; Haskins et al., 2020).”
BackgroundOu, Y., Zöllei, L., Retzepi, K., Castro, V. M., Bates, S. V., Pieper, S., Andriole, K. P., Murphy, S. N., Gollub, R. L., & Grant, P. E. (2017) · Human Brain Mapping · doi:10.1002/hbm.23573 · Cited by 36
“Of the many available registration algorithms [Klein et al., 2009; Ou et al., 2014; Sotiras et al., 2013], we chose the DRAMMS pair-wise registration software [Ou et al., 2011] (https://www.nitrc.org/projects/ dramms) and its group-wise registration version specifically programmed for unbiased…”
MethodMultimodal Medical Image Registration Based on Feature Spheres in Geometric Algebra
Cao, W., Lyu, F., He, Z., Cao, G., & He, Z. (2018) · IEEE Access · doi:10.1109/access.2018.2818403 · Cited by 36
“In general, an image registration algorithm has three components: 1) a deformationmodel, 2) an objective function and 3) an optimization method [4].”
MethodMITT: Medical Image Tracking Toolbox
Queirós, S., Morais, P., Barbosa, D. C., Fonseca, J. C., Vilaça, J. L., & D’hooge, J. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2840820 · Cited by 36
“[4]).”
Schipaanboord, B., Boukerroui, D., Peressutti, D., van Soest, J., Lustberg, T., Dekker, A. L. A. J., van Elmpt, W., & Gooding, M. J. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2907072 · Cited by 36
“The literature on medical image registration is enormous [8] and popular DIR algorithms such as optical flow [9], Demons [10], and free-form-deformations [11] have been”
BackgroundMang, A., Gholami, A., Davatzikos, C. A., & Biros, G. (2019) · SIAM Journal on Scientific Computing · doi:10.1137/18m1207818 · Cited by 36
Self-citationUncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior
Laves, M.-H., Tölle, M., & Ortmaier, T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60365-6_9 · Cited by 35
Recurrent Tissue-Aware Network for Deformable Registration of Infant Brain MR Images
Wei, D., Ahmad, S., Guo, Y., Chen, L., Huang, Y., Ma, L., Wu, Z., Li, G., Wang, L., Lin, W., et al. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3137280 · Cited by 34
Deformable registration of CT and cone-beam CT with local intensity matching
Park, S., Plishker, W. L., Quon, H., Wong, J. W., Shekhar, R., & Lee, J. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa4f6d · Cited by 35
Diffeomorphic Image Registration with Neural Velocity Field
Han, K., Sun, S., Yan, X., You, C., Tang, H., Naushad, J., Ma, H., Kong, D., & Xie, X. S. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00191 · Cited by 37
“However, these methods usually generate highaccuracy results at the cost of slow speed and intensive computation, and the performance may vary under different modeling assumptions [42].”
BackgroundMethodSmolders, A., Lomax, A., Weber, D. C., & Albertini, F. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad0282 · Cited by 34
Zhang, R., Mo, H., Wang, J., Jie, B., He, Y., Jin, N., & Zhu, L. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3467919 · Cited by 34
“D EFORMABLE medical image registration [1] is a fundamental medical image analysis task.”
A hybrid biomechanical intensity based deformable image registration of lung 4DCT
Samavati, N., Velec, M., & Brock, K. K. (2015) · Physics in Medicine and Biology · doi:10.1088/0031-9155/60/8/3359 · Cited by 34
Recent Advances on 2D and 3D Change Detection in Urban Environments from Remote Sensing Data
Karantzalos, K. G. (2014) · Springer eBooks · doi:10.1007/978-3-319-11469-9_10 · Cited by 34
Fully-automatic left ventricular segmentation from long-axis cardiac cine MR scans
Shahzad, R., Tao, Q., Dzyubachyk, O., Staring, M., Lelieveldt, B. P. F., & van der Geest, R. J. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.04.004 · Cited by 34
“Two recent review papers by McClelland et al. (2013) and275 Sotiras et al. (2013) summarize these methods.”
BackgroundIRINA: Iris Recognition (Even) in Inaccurately Segmented Data
Proença, H. P. M. C., & Neves, J. C. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.714 · Cited by 34
“[25] provide an overview of the state-of-the-art in image registration.”
BackgroundDaubechies wavelet‐based local feature descriptor for multimodal medical image registration
Yelampalli, P. K. R., Nayak, J., & Gaidhane, V. H. (2018) · IET Image Processing · doi:10.1049/iet-ipr.2017.1305 · Cited by 34
A Novel Diffeomorphic Model for Image Registration and Its Algorithm
Zhang, D., & Chen, K. (2018) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-018-0811-3 · Cited by 34
“For multi-modal registration, finding a suitable distance measure is the most essential step [22,35,36,47,57].”
BackgroundInfluentialLiu, X., Jiang, D., Wang, M., & Song, Z. (2018) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-018-1924-y · Cited by 34
A Practical Review on Medical Image Registration: From Rigid to Deep Learning Based Approaches
Andrade, N., Faria, F. A., & Cappabianco, F. A. M. (2018) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi.2018.00066 · Cited by 36
“This property is implemented by the so called diffeomorphic registration methods [30].”
MethodFu, W., Sharma, S., Abadi, E., Iliopoulos, A.-S., Wang, Q., Lo, J. Y.-C., Sun, X., Segars, W. P., & Samei, E. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3063080 · Cited by 34
A comparative study of automatic image segmentation algorithms for target tracking in MR‐IGRT
Feng, Y., Kawrakow, I., Olsen, J., Parikh, P. J., Noel, C. E., Wooten, O., Du, D., Mutic, S., & Hu, Y. (2016) · Journal of Applied Clinical Medical Physics · doi:10.1120/jacmp.v17i2.5820 · Cited by 34
“(9,10) Atlas-based segmentation transforms an established ROI on the reference image (atlas) to the image to be segmented using rigid or deformable image registration.(11,12) Although many methods in the first category were investigated for automated segmentation of MR images,(9,13) most of the published studies were…”
MethodRetinal OCT Image Registration: Methods and Applications
Pan, L., & Chen, X. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2021.3110958 · Cited by 33
“In the two review papers on medical image registration, retinal image registration was briefly mentioned [7], [23].”
BackgroundEstimating medical image registration error and confidence: A taxonomy and scoping review
Bierbrier, J. L., Gueziri, H.-E., & Collins, D. L. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102531 · Cited by 33
Geometric Graph Matching Using Monte Carlo Tree Search
Pinheiro, M. A., Kybic, J., & Fua, P. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2636200 · Cited by 33
“This makes it impractical to use traditional registration techniques that rely on maximizing image similarity [1], [2], [3].”
MethodTD-Net:unsupervised medical image registration network based on Transformer and CNN
Song, L., Liu, G., & Ma, M. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03472-w · Cited by 33
“Deformable image registration (DIR) aims to establish a dense non-linear correspondence between a pair of images through a spatial transformation model [1].”
BackgroundZheng, J.-Q., Wang, Z., Huang, B., Lim, N. H., & Papież, B. W. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.103038 · Cited by 32
“Alignment of two images, also known as image registration [23], is an important task in computer vision applications.”
BackgroundTan, M., Li, Z., Qiu, Y., McMeekin, S., Thai, T., Ding, K., Moore, K. N., Liu, H., & Zheng, B. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2473823 · Cited by 32
“The goal of image registration is to determine the spatial transform that maps points from one image to homologous points on an object in the second image [19], [20].”
BackgroundVakalopoulou, M., Karantzalos, K. G., Nikos Komodakis, A., & Nikos Paragios, A. (2016) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2016.2557081 · Cited by 32
“For tackling the registration problem, we employ a nonrigid framework based on recently proposed and validated algorithms [16], [19], [31].”
BackgroundMethodIndirect Image Registration with Large Diffeomorphic Deformations
Chen, C., & Öktem, O. (2018) · SIAM Journal on Imaging Sciences · doi:10.1137/17m1134627 · Cited by 32
“See also [57] for further examples from medical imaging.”
BackgroundAdvances in computational and statistical diffusion MRI
O’Donnell, L. J., Daducci, A., Wassermann, D., & Lenglet, C. (2017) · NMR in Biomedicine · doi:10.1002/nbm.3805 · Cited by 32
Registration and Fusion of Close-Range Multimodal Wheat Images in Field Conditions
Dandrifosse, S., Carlier, A., Dumont, B., & Mercatoris, B. (2021) · Remote Sensing · doi:10.3390/rs13071380 · Cited by 32
Amstutz, F., Nenoff, L., Albertini, F., Ribeiro, C. O., Knopf, A. C., Unkelbach, J., Weber, D. C., Lomax, A., & Zhang, Y. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/abf8f5 · Cited by 32
“There are many different DIR algorithms proposed in the literature and in use for medical applications, particularly in radiotherapy ( Maintz and Viergever 1998, Sotiras et al 2013, Oh and Kim 2017 ) .”
Li, H., Yong Fan, A., & Initiative, A. D. N. (2022) · Human Brain Mapping · doi:10.1002/hbm.25782 · Cited by 32
“…procedure to estimate the velocity and deformation fields progressively from coarse to fine spatial resolutions for its effectiveness for handling large deformation between images, as demonstrated in conventional image registration algorithms (Sotiras et al., 2013; Viergever et al., 2016).”
BackgroundMethodInfluentialA Biomechanical Modeling Guided CBCT Estimation Technique
Zhang, Y., Tehrani, J. N., & Wang, J. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2623745 · Cited by 32
“the right side of the equation, the DVF v is defined on the new C BCT image voxel grids and points to μprior [29].”
Yu, D., Yang, F., Yang, C., Leng, C., Cao, J., Wang, Y., & Tian, J. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2465855 · Cited by 31
“Feature extraction and feature matching are two main crucial steps for feature-based registration due to their determinative effects on algorithm efficiency and robustness [14].”
BackgroundSurface deformation analysis of collapsed lungs using model-based shape matching
Nakao, M., Tokuno, J., Chen-Yoshikawa, T. F., Date, H., & Matsuda, T. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-019-02013-0 · Cited by 31
Computer‐aided 3D analysis of anatomy and radiographic parameters of the distal radius
Suojärvi, N., Tampio, J., Lindfors, N. C., & Waris, E. (2020) · Clinical Anatomy · doi:10.1002/ca.23615 · Cited by 31
“was obtained by using deformable shape models (Sotiras et al., 2013).”
MethodKaur, H. P., Kaur, N., & Neeru, N. (2022) · Displays · doi:10.1016/j.displa.2022.102223 · Cited by 34
Gradient-based subspace phase correlation for fast and effective image alignment
Ren, J., Vlachos, T., Zhang, Y., Zheng, J., & Jiang, J. (2014) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2014.07.001 · Cited by 31
“Apart from a comprehensive survey in [8], some more recent papers can be found in [9-12], though they mainly focus on particular topics such as deformable medical image registration [9], remote sensing image registration [10], evolutionary image registration methods for 3D modeling [11] and 2D/3D registration methods…”
BackgroundAnalysis of macular OCT images using deformable registration
Chen, M., Lang, A., Ying, H. S.-C., Calabresi, P. A., Prince, J. L., & Carass, A. (2014) · Biomedical Optics Express · doi:10.1364/boe.5.002196 · Cited by 31
Registration Technology of Augmented Reality in Oral Medicine: A Review
Jiang, J., Huang, Z., Qian, W., Zhang, Y., & Liu, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2912949 · Cited by 31
Automatic Descriptor-Based Co-Registration of Frame Hyperspectral Data
Vakalopoulou, M., & Karantzalos, K. G. (2014) · Remote Sensing · doi:10.3390/rs6043409 · Cited by 30
“Several registration approaches have been proposed and detailed in the computer vision and remote sensing literature ([10,11] and the references therein).”
BackgroundFeature-based multi-resolution registration of immunostained serial sections
Lobachev, O., Ulrich, C., Steiniger, B. S., Wilhelmi, V., Stachniss, V., & Guthe, M. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.07.010 · Cited by 30
Lv, G., Teng, S. W., & Lu, G. (2016) · Pattern Recognition Letters · doi:10.1016/j.patrec.2016.09.011 · Cited by 30
Mid-space-independent deformable image registration
Aganj, I., Iglesias, J. E., Reuter, M., Sabuncu, M. R., & Fischl, B. R. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.055 · Cited by 30
“Linear transformation is often not sufficient to account for cross-subject anatomical variation or temporal changes in an individual anatomy, thereby making deformable image registration (Sotiras et al., 2013) a necessary part of most analysis pipelines.”
Multimodal image registration using Laplacian commutators
Zimmer, V. A., Ballester, M. Á. G., & Piella, G. (2018) · Information Fusion · doi:10.1016/j.inffus.2018.09.009 · Cited by 30
“Classical registration methods can be divided into feature-and intensity-based methods [15, 16].”
MethodChen, J., Li, Y., Du, Y., & Frey, E. C. (2020) · Medical Physics · doi:10.1002/mp.14545 · Cited by 30
“1) on a single image pair [17]:”
Enhancing medical image registration via appearance adjustment networks
Meng, M., Bi, L., FULHAM, M. J., Feng, D. D., & Kim, J. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119444 · Cited by 30
On-line 3 D motion estimation using low resolution MRI
Glitzner, M., de Senneville, B. D., Lagendijk, J. J. W., Raaymakers, B. W., & Crijns, S. P. M. (2015) · Physics in Medicine and Biology · doi:10.1088/0031-9155/60/16/n301 · Cited by 30
Nair, R. R., & Babu, T. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.04.121 · Cited by 29
Randomly Perturbed B-Splines for Nonrigid Image Registration
Sun, W., Niessen, W. J., & Klein, S. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2598344 · Cited by 29
“N ONRIGID image registration is a commonly used technique in medical image analysis [1], [2], [3], [4], [5].”
MethodNon-rigid registration based model-free 3D facial expression recognition
Savran, A., & Sankur, B. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.07.005 · Cited by 30
“We refer Sotiras et al. (2013) for a detailed discussion on he deformation models including the locally affine models.”
InfluentialAccelerated nonrigid image registration using improved Levenberg–Marquardt method
Dong, J., Lü, K., Xue, J., Dai, S., Zhai, R., & Pan, W. (2017) · Information Sciences · doi:10.1016/j.ins.2017.09.059 · Cited by 29
Image Registration in Medical Robotics and Intelligent Systems: Fundamentals and Applications
Liu, J., Singh, G., Alaref, S. J., Lee, B., Oleru, O. O., Min, J. K., Dunham, S. N., Sabuncu, M. R., & Mosadegh, B. (2019) · Advanced Intelligent Systems · doi:10.1002/aisy.201900048 · Cited by 29
Machado, I. P., Toews, M., George, E., Unadkat, P., Essayed, W. I., Luo, J., Teodoro, P., Carvalho, H., Martins, J. M., Golland, P., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116094 · Cited by 29
Biomechanics-Informed Neural Networks for Myocardial Motion Tracking in MRI
Qin, C., Wang, S., Chen, C., Qiu, H., Bai, W., & Rueckert, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59716-0_29 · Cited by 34
Fibronectin-based nanomechanical biosensors to map 3D surface strains in live cells and tissue
Shiwarski, D. J., Tashman, J. W., Tsamis, A., Bliley, J. M., Blundon, M. A., Aranda-Michel, E. C., Jallerat, Q., Szymanski, J. M., McCartney, B. M., & Feinberg, A. W. (2020) · Nature Communications · doi:10.1038/s41467-020-19659-z · Cited by 29
Study on anatomical and functional medical image registration methods
Gupta, S., Gupta, P., & Verma, V. S. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.08.085 · Cited by 29
An image registration method for voxel-wise analysis of whole-body oncological PET-CT
Jönsson, H., Ekström, S., Strand, R., Pedersen, M. A., Molin, D. L., Ahlström, H. K., & Kullberg, J. (2022) · Scientific Reports · doi:10.1038/s41598-022-23361-z · Cited by 29
Fast graph-cut based optimization for practical dense deformable registration of volume images
Ekström, S., Malmberg, F., Ahlström, H. K., Kullberg, J., & Strand, R. (2020) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2020.101745 · Cited by 29
Non-rigid registration of serial section images by blending transforms for 3D reconstruction
Kajihara, T., Funatomi, T., Makishima, H., Aoto, T., Kubo, H., Yamada, S., & Mukaigawa, Y. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.07.001 · Cited by 30
“This approach has been studied frequently in the image processing field [12,13] and other fields [14– 16] .”
Ciuti, G., Webster, R. J., III, Kwok, K., & Menciassi, A. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00294-6 · Cited by 35
“In contemporary clinical practice, the CAS workflow primarily finds its application in orthopaedic surgery as it involves the registration of rigid bodies, that is, bones; however, the registration of deformable structures remains a challenge 107,108 .”
SELF-SUPERVISED MRI TISSUE SEGMENTATION BY DISCRIMINATIVE CLUSTERING
Gonçalves, N., Nikkilä, J., & Vigário, R. (2013) · International Journal of Neural Systems · doi:10.1142/s012906571450004x · Cited by 28
Learning-Based Multimodal Image Registration for Prostate Cancer Radiation Therapy
Cao, X., Gao, Y., Yang, J., Guorong Wu, A., & Shen, D. G. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46726-9_1 · Cited by 28
Weakly Supervised Learning of Metric Aggregations for Deformable Image Registration
Ferrante, E., Dokania, P. K., Silva, R. M., & Nikos Paragios, A. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2869700 · Cited by 28
“When the observed images are mapped through a nonlinear dense transformation or a spatially varying deformation model, we refer to this problem as deformable image registration (DIR) [1].”
BackgroundGomez, A. D., Knutsen, A. K., Xing, F., Lu, Y.-C., Chan, D. D., Pham, D. L., Bayly, P. V., & Prince, J. L. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2874591 · Cited by 29
Sahyoun, C. C., Subhash, H. M., Peru, D., Ellwood, R. P., & Pierce, M. C. (2019) · Caries Research · doi:10.1159/000502375 · Cited by 28
“…intraoral scanning or cone beam computed tomography, in order to provide multimodal information on the same region of tooth; image scaling, deformable registration, and fusion methods [Sotiras et al., 2013] would have to be implemented in order to superimpose OCT data onto another established 3-”
MethodOCTRexpert: A Feature-Based 3D Registration Method for Retinal OCT Images
Pan, L., Shi, F., Xiang, D., Yu, K., Duan, L., Zheng, J., & Chen, X. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2967589 · Cited by 28
“MEDICAL image registration has attracted more and more attention due to its valuable applications in clinical studies [1].”
BackgroundSiracusano, G., Corte, A. L., Gaeta, M., Cicero, G., Chiappini, M., & Finocchio, G. (2020) · Sustainability · doi:10.3390/su12208573 · Cited by 38
“FABEMD has been used for different purposes in medical imaging [24] including image retrieval [25] and fusion [26,27].”
MethodLung‐CRNet: A convolutional recurrent neural network for lung 4DCT image registration
Lu, J., Jin, R., Song, E., Ma, G., & Wang, M. (2021) · Medical Physics · doi:10.1002/mp.15324 · Cited by 29
Mang, A., & Biros, G. (2017) · SIAM Journal on Scientific Computing · doi:10.1137/16m1070475 · Cited by 28
“For multimodal registration problems we have to replace the L2-distance in (1a) with more involved distance measure; examples can be found in [47, 53].”
BackgroundMethodReview on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
Brea, L. S., Jesus, D. A. D., Shirazi, M. F., Pircher, M., van Walsum, T., & Klein, S. (2019) · Applied Sciences · doi:10.3390/app9132700 · Cited by 28
“Some publicly-available registration frameworks have been made available to facilitate the research in medical image registration [114] as well as the comparison of different registration procedures.”
MethodTransport-based analysis, modeling, and learning from signal and data distributionsPreprint
Kolouri, S., Park, S., Thorpe, M., Slepčev, D., & Rohde, G. K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.04767 · Cited by 28
“Comprehensive recent surveys on image registration can be found in [149], [34], [115].”
BackgroundMethodKhallaghi, S., Sánchez, C. A., Rasoulian, A., Nouranian, S., Romagnoli, C., Abdi, H., Chang, S. D., Black, P. C., Goldenberg, L., Morris, W. J., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2443978 · Cited by 25
“[1], [2]).”
Pai, A., Sommer, S., Sørensen, L., Darkner, S., Jon Sporring, A., & Nielsen, M. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2511062 · Cited by 27
“For a discussion about commonly used constraints on deformation models, see [10].”
BackgroundMethodZachiu, C., de Senneville, B. D., Moonen, C., Raaymakers, B., & Ries, M. (2018) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aad109 · Cited by 25
Accelerating B-spline interpolation on GPUs: Application to medical image registration
Zachariadis, O., Teatini, A., Satpute, N., Gómez-Luna, J., Mutlu, O., Elle, O. J., & Olivares, J. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105431 · Cited by 27
“com (Andrea Teatini) that has been developed to reproduce and model such non-linear deformations [4].”
BackgroundDeep learning based data-adaptive descriptor for non-rigid multi-modal medical image registration
Zhu, F., Zhu, X., Huang, Z., Ding, M., Li, Q., & Zhang, X. (2021) · Signal Processing · doi:10.1016/j.sigpro.2021.108023 · Cited by 29
Unsupervised Multistep Deformable Registration of Remote Sensing Imagery Based on Deep Learning
Papadomanolaki, M., Christodoulidis, S., Karantzalos, K. G., & Vakalopoulou, M. (2021) · Remote Sensing · doi:10.3390/rs13071294 · Cited by 27
Joint Detection and Matching of Feature Points in Multimodal Images
Baruch, E. B., & Keller, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3092289 · Cited by 27
Generative myocardial motion tracking via latent space exploration with biomechanics-informed prior
Qin, C., Wang, S., Chen, C., Bai, W., & Rueckert, D. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102682 · Cited by 29
Image Registration for Quantitative Parametric Response Mapping of Cancer Treatment Response
Boes, J. L., Hoff, B. A., Hylton, N. M., Pickles, M. D., Turnbull, L. W., Schott, A. F., Rehemtulla, A., Chamberlain, R., Lemasson, B., Chenevert, T. L., et al. (2014) · Translational Oncology · doi:10.1593/tlo.14121 · Cited by 27
“A wealth of registration options, cost metrics, and deformation functions are available (Table 1) and are summarized in the literature [24,28].”
BackgroundA Deep Metric for Multimodal RegistrationPreprint
Simonovsky, M., Gutiérrez-Becker, B., Mateus, D., Navab, N., & Nikos Komodakis, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.05396 · Cited by 27
“Multimodal registration is a very challenging problem in medical imaging commonly faced during image-guided interventions and data fusion [13].”
Synth-by-Reg (SbR): Contrastive Learning for Synthesis-Based Registration of Paired Images
Casamitjana, A., Mancini, M., & Iglesias, J. E. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87592-3_5 · Cited by 2
DREDge: robust motion correction for high-density extracellular recordings across species
Windolf, C., Yu, H. Y., Paulk, A. C., Meszéna, D., Muñoz, W., Boussard, J., Hardstone, R., Caprara, I., Jamali, M., Kfir, Y., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02614-5 · Cited by 32
“High-density microelectrode arrays (MEAs), in particular Neuropixels (NP) probes, have enabled simultaneous high-quality recording from large populations (hundreds) of neurons with high resolution in time (20–30 kHz) and space (channels spaced by tens of microns or less) 1–6 .”
Non-rigid retinal image registration using an unsupervised structure-driven regression network
Zou, B., He, Z., Zhao, R., Zhu, C., Liao, W., & Li, S. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.122 · Cited by 26
“Comprehensive overviews of registration methods and the most recent advances in this domain can be found in [10–12].”
MethodEdge-Aware Pyramidal Deformable Network for Unsupervised Registration of Brain MR Images
Cao, Y., Zhu, Z., Rao, Y., Qin, C., Lin, D., Dou, Q., Ni, D., & Wang, Y. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.620235 · Cited by 26
“Deformable image registration is to perform spatial transformation between a pair of images and establish a non-linear point-wise correspondence to achieve spatial consistency (Sotiras et al., 2013).”
BackgroundRegions Preserving Edge Enhancement for Multisensor-Based Medical Image Fusion
He, K., Gong, J., Xie, L., Zhang, X., & Xu, D. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3066467 · Cited by 26
MRSI: A multimodal proximity remote sensing data set for environment perception in rail transit
Chen, Y., Zhu, N., Wu, Q., Wu, C., Niu, W., & Wang, Y. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22801 · Cited by 26
Faragallah, O. S., El‐Hoseny, H. M., El‐Shafai, W., El‐Rahman, W. A., El‐sayed, H. S., El‐Rabaie, E. M., El-Samie, F. A., Mahmoud, K. R., & Geweid, G. G. N. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12260-0 · Cited by 26
RetractedSimilarity attention-based CNN for robust 3D medical image registration
Zhu, F., Wang, S., Li, D., & Li, Q. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104403 · Cited by 26
Zhang, Y., Nie, R., Cao, J., & Ma, C. (2023) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2022.3223487 · Cited by 26
“Different medical imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and single-photon emission tomography (SPET) present dense structures, soft tissues, metabolism, or other different information for the human body [2], [3], [4].”
BackgroundLee, J., Cai, X., Schönlieb, C., & Coomes, D. A. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2431692 · Cited by 26
A novel variational model for image registration using Gaussian curvature
Ibrahim, M., Chen, K., & Loeza, C. F. B. (2014) · Geometry Imaging and Computing · doi:10.4310/gic.2014.v1.n4.a2 · Cited by 26
Confidence Estimation for Medical Image Registration Based On Stereo Confidences
Saygili, G., Staring, M., & Hendriks, E. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2481609 · Cited by 29
“[3] described different features for image registration many of which are also used in stereo matching.”
BackgroundA neural network approach for fast, automated quantification of DIR performance
Neylon, J., Min, Y., Low, D. A., & Santhanam, A. P. (2017) · Medical Physics · doi:10.1002/mp.12321 · Cited by 28
“Deformable image registration (DIR) allows image-guided analyses of nonrigid anatomical variations.(12,13) DIR has multiple applications in ART,(14) including dose accumulation and contour propagation.”
BackgroundDeformable image registration applied to lung SBRT: Usefulness and limitations
Sarrut, D., Baudier, T., Ayadi, M., Tanguy, R., & Rit, S. (2017) · Physica Medica · doi:10.1016/j.ejmp.2017.09.121 · Cited by 26
“Ref Comments [1] DIR evaluation review [2] Empire database [3] MIDRAS database [4] Dir-Lab database [5] Popi-model database [6, 7, 8, 9, 10, 11] Compare performances of DIR methods [12, 13, 14, 15, 16, 17] Evaluation assessments [18, 19, 20, 21, 22] Some deformable phantoms [23, 24, 25, 26, 27] Sliding issue”
BackgroundMethodFeng, Y., Ding, M., & Zhang, X. (2019) · Sensors · doi:10.3390/s19214675 · Cited by 26
Image registration for 3D medical images
Nair, R. R., & Babu, T. (2024) · Advances in computers · doi:10.1016/bs.adcom.2024.03.002 · Cited by 25
Metadata
- Authors
- Sotiras, A., Davatzikos, C., & Paragios, N.
- Affiliations
- University of Pennsylvania (US)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2013-07
- Volume
- 32
- Issue
- 7
- Pages
- 1153-1190
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- PMC
- PMC3745275
- OpenAlex
- W2115167851
- Cited by
- 1,587
- References
- 539
- Citations by year
- 2026: 81 · 2025: 121 · 2024: 115 · 2023: 96 · 2022: 115 · 2021: 142 · 2020: 136 · 2019: 126 · 2018: 152 · 2017: 153 · 2016: 130 · 2015: 123 · 2014: 86 · 2013: 9
- FWCI
- 18.05
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- HAL (submittedVersion, other-oa)
- Funders
- National Institute on Aging (R01AG014971)
- National Institute of Mental Health (R01MH070365)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- deformable image registration, medical image registration, multimodal image fusion, statistical atlas, population models, anatomical variation
- MeSH terms
- Algorithms, Diagnostic Imaging, Humans, Image Processing, Computer-Assisted
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